Improvement of Erosion-Corrosion Behavior of AISI 420 Stainless Steel by Ion-Assisted Deposition ZrN Coatings
Bibliographic record
Abstract
In this paper, a pragmatic technique has been developed to evaluate the erosion-corrosion behavior of three kinds of ZrN coatings (i.e., monolayer, multilayer, and gradient layers) which were deposited on AISI 420 martensitic stainless steel using an ion-assisted deposition technology. Among them, the monolayer coating refers to the coating with no change in composition and structure, the multilayer coating refers to the coating with alternating change of Zr/ZrN, and the gradient coating refers to the ZrN coating by increasing N2 partial pressure gradually. The morphology, composition, and microhardness of these ZrN coatings were examined by means of integrating the scanning electron microscopy (SEM), X-ray diffraction (XRD), and Knoop hardness measurements, while anodic polarization tests and salt fog spray tests in a simulated industrial environment have been performed to evaluate and identify the corrosion mechanisms of these coatings. The surface microhardness and corrosion resistance of the AISI420 martensitic stainless steel is found to be significantly improved by depositing the ion-assisted deposition ZrN coatings. The study indicates that the erosion-corrosion behavior in the slurry is the result of the synergistic effect of small-angle erosion and acid solution corrosion. Three ZrN coatings hinder the slurry erosion-corrosion behavior from two aspects (i.e., erosion resistance of small-angle particles as well as corrosion resistance of the substrate), thereby significantly improving the erosion-corrosion resistance of AISI 420 stainless steel. In addition, the ZrN gradient coatings show a much better erosion-corrosion resistance than that of the monolayer/multilayer ZrN coating because they have excellent crack resistance, bearing capacity, and electrochemical performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".